Using Cluster Analysis to Identify Subgroups of College Students at Increased Risk for Cardiovascular Disease

Background: College students are considered a vulnerable population because they are exposed to many health issues such as sexually transmitted diseases, alcohol abuse, and chronic diseases. According to the 2008 National College Health Risk Behavior Survey, approximately 35% of college students are overweight or obese; weight gain during college years was greater compared to weight gain among the general young adult population.

Purpose: To examine the co-occurrence of cardiovascular risk factors and cluster subgroups of college students, ages 19 to 39, for cardiovascular risks based on socio-demographics, non-modifiable and modifiable risk factors. The overall goal is to identify a target group of individuals at increased risk for cardiovascular disease.

Conceptual Framework: The conceptual models guiding this study were the Health Belief Model and the Information, Motivation and Behavior Skill Model.

Method: A cross-sectional, descriptive study was conducted using co-occurrence patterns and hierarchical clustering analysis. A total of 158 college students, who attended a Midwestern university, aged 19 to 39 years (M = 24.3, SD = 4.6) were in the final sample. The study variables included socio-demographics and health history, biometric measurements (height, weight, body mass index, blood pressure, blood glucose, lipid panel), and risk assessments (30-year cardiovascular disease risk and lifetime atherosclerotic cardiovascular disease risk).

Results: More than half of the participants were male (54.4%, n = 86) and White (63.1%, n = 99). Approximately 32.3% (n = 51) of the participants reported having a family history of heart disease. The average 30-year hard cardiovascular risk assessment was 2.3%, the 30-year full cardiovascular risk assessment was 4.8%, and the lifetime risk estimate was 31.4%. More than 50% of participants had one or more cardiovascular risk factors; the most commonly occurring cardiovascular risk factors were overweight/obese and hypertension (10.8%, n = 17). Of the total 34 risk factors that co-occurred, 30 of them involved being overweight/obese. Using hierarchical clustering analysis, seven-cluster-solution was obtained. Three clusters displayed significant relationships related to the lifetime and 30-year cardiovascular disease risks.

Conclusions: Detecting high-risk groups through a clustering technique can be used to identify groups of college students to target for interventions. The hierarchical cluster analysis identified White, single males with a family history of heart disease, overweight/obese, hypertensive, and occasionally (weekly) consumed red meat were considered the higher risk group to target for a cardiovascular risk reduction intervention compared to other subgroups. Health care providers such as nurses should use this information to initiate conversations regarding health promotion and intervention in the high-risk individuals.